Adaptive Vibrarthographic Signal Denoising via Ant Colony Optimization Using Dynamic Denoising Filter Parameters

This study proposes an ant colony optimization (ACO) denoising method with dynamic filter parameters. The proposed method is developed based on ensemble empirical mode decomposition (EEMD), and aims to improve the quality of vibrarthographic (VAG) signals. It mixes the original VAG signals with different white noise amplitudes, and adopts a hybrid technology that combines EEMD with a Savitzky-Golay (SG) filter containing the dynamic parameters optimized by ACO. The results show that the proposed method provides a higher peak signal-to-noise ratio (PSNR) and a smaller root-mean-square difference than the regular methods. The SNR improvement for the VAG signals of normal knees can reach 13 dB while maintaining the original signal structure, and the SNR improvement for the VAG signals of abnormal knees can reach 20 dB. The method proposed in this study can improve the quality of nonstationary VAG signals.

Numerical Integration-Based Performance Analysis of Cross-Eye Jamming Algorithm

In this paper, performance analysis of the cross-eye jamming effect under mechanical defects is dealt with. By using a numerical analysis-based approach, the performance analysis method proposed in this paper is closer to the not approximated empirical mean square difference (MSD) than the first-order Taylor approximation-based performance analysis method and the second-order Taylor approximation-based performance analysis method proposed in previous studies. In other words, the effects of amplitude ratio perturbation and phase difference perturbation on performance degradation are quantitatively analyzed. Note that the numerical integration is adopted to derive an analytic expression of the MSD.

Influence of river discharge on circulation and tidal process in the Java Sea, Indonesia

This study investigates the effect of river discharge in transport and tidal processes in the Java Sea using the Coastal and Regional Ocean Community (CROCO) hydrodynamic model. The model has 20 vertical layers and a horizontal resolution of 1/18 degrees. The oceanic and atmospheric forcing of this model is taken from the global Copernicus Marine Environment Monitoring Service (CMEMS) model and the fifth generation ECMWF atmospheric reanalysis (ERA5) hourly data. Daily Global Flood Awareness System (GloFAS) data has been successfully implemented as river flow data for this study. Two scenarios have been applied, namely, with and without river discharge. This study shows that the two scenarios and the satellite observational data agree in terms of water level with Root Mean Square Difference RMSD) about 4 cm, Sea Surface Temperature with RMSD about 0.29 °C, and Sea Surface Salinity with RMSD about 0.39 psu. The model was also validated using seven tide gauges and produced a good agreement. River discharge increase eastward transport in the eastern part of the Java Sea up to 0.1 Sv (1 Sv= 106 m3s−1). Both scenarios produce similar tidal amplitude and phase and agree well with previous studies and other tidal data sources.

A Slight Temperature Warming Trend Occurred over Lake Ontario from 2001 to 2018

Satellite-derived lake surface water temperature (LSWT) measurements can be used for monitoring purposes. However, analyses based on the LSWT of Lake Ontario and the surrounding land surface temperature (LST) are scarce in the current literature. First, we provide an evaluation of the commonly used Moderate Resolution Imaging Spectroradiometer (MODIS)-derived LSWT/LST (MOD11A1 and MYD11A1) using in situ measurements near the area of where Lake Ontario, the St. Lawrence River and the Rideau Canal meet. The MODIS datasets agreed well with ground sites measurements from 2015–2017, with an R2 consistently over 0.90. Among the different ground measurement sites, the best results were achieved for Hill Island, with a correlation of 0.99 and centered root mean square difference (RMSD) of 0.73 K for Aqua/MYD nighttime. The validated MODIS datasets were used to analyze the temperature trend over the study area from 2001 to 2018, through a linear regression method with a Mann–Kendall test. A slight warming trend was found, with 95% confidence over the ground sites from 2003 to 2012 for the MYD11A1-Night datasets. The warming trend for the whole region, including both the lake and the land, was about 0.17 K year−1 for the MYD11A1 datasets during 2003–2012, whereas it was about 0.06 K year−1 during 2003–2018. There was also a spatial pattern of warming, but the trend for the lake region was not obviously different from that of the land region. For the monthly trends, the warming trends for September and October from 2013 to 2018 are much more apparent than those of other months.

Denoising the ECG Signal Using Ensemble Empirical Mode Decomposition

In this paper, a novel electrocardiogram (ECG) denoising method based on the Ensemble Empirical Mode Decomposition (EEMD) is proposed by introducing a modified customized thresholding function. The basic principle of this method is to decompose the noisy ECG signal into a series of Intrinsic Mode Functions (IMFs) using the EEMD algorithm. Moreover, a modified customized thresholding function was adopted for reducing the noise from the ECG signal and preserve the QRS complexes. The denoised signal was reconstructed using all thresholded IMFs. Real ECG signals having different Additive White Gaussian Noise (AWGN) levels were employed from the MIT-BIH database to evaluate the performance of the proposed method. For this purpose, output SNR (SNRout), Mean Square Error (MSE), and Percentage Root mean square Difference (PRD) parameters were used at different input SNRs (SNRin). The simulation results showed that the proposed method provided significant improvements over existing denoising methods.

Intercomparisons of Tropospheric Wind Velocities Measured by Multi-Frequency Wind Profilers and Rawinsonde

Concurrent measurements of three-dimensional wind velocities made with three co-located wind profilers operated at frequencies of 52 MHz, 449 MHz, and 1.29 GHz for the period 12–16 September 2017 are compared for the first time in this study. The velocity–azimuth display (VAD) method is employed to estimate the wind velocities. The result shows that, in the absence of precipitation, the root mean square difference (RMSD) in the horizontal wind speed velocities U and wind directions D between different pairs of wind profilers are, respectively, in the range of 0.94–0.99 ms−1 and 7.7–8.3°, and those of zonal wind component u and meridional wind component v are in the respective ranges of 0.91–1.02 ms−1 and 1.1–1.24 ms−1. However, the RMSDs between wind profilers and rawinsonde are in the range of 2.89–3.26 ms−1 for horizontal wind speed velocity and 11.17–14.48° for the wind direction, which are around 2–3 factors greater than those between the wind profilers on average. In addition to the RMSDs, MDs between wind profilers and radiosonde are around one order of magnitude larger than those between wind profilers. These results show that the RMSDs, MDs, and Stdds between radars are highly consistent with each other, and they are much smaller than those between radar and rawinsonde. This therefore suggests that the wind profiler-measured horizontal wind velocities are much more reliable, precise, and accurate than the rawinsonde measurement.

Implications of the SARS-Cov-2 Pandemic for Mortality Forecasting: Case Study for the Czech Republic and Spain

The current pandemic situation of SARS-Cov-2 is negatively influencing people worldwide, and leading to high mortality and excess mortality, due to more reasons than only the disease itself. Thus, forecasting of the mortality rates and consequent population projections would have been complicated since 2020. Paper models mortality in the Czech Republic and Spain and assesses the possible impact of the COVID-19 on the forecasts. We use a Lee–Carter model and apply it to data from 1981 to 2019 (forecast A) and 1981 to 2020 (forecast B). Our results show differences in forecasts up to 2030 by mean square difference. The highest is in ages above 50 for Spain, where it was observed that the COVID-19 pandemic affected the mortality rates in a way that they were higher, and decreased at a slower pace than they would without taking 2020 into account. In the Czech Republic (CR), the forecast does not seem to be affected yet, but it could be in the future when the number of deaths (not only due to COVID-19, but altogether) increases significantly. Nevertheless, we have to verify our preliminary results on real data as soon as they are available.

Evaluation of the J-OFURO3 Sea Surface Net Radiation and Inconsistency Correction

A new satellite-based product containing daily sea surface net radiation () values at a spatial resolution of 0.25° from 1988 to 2013, named the Japanese Ocean Flux Data Sets with Use of Remote Sensing Observations, version 3 (J-OFURO3), was recently generated and released. In this letter, the performance of the J-OFURO3 sea-surface product was fully evaluated by using observations from 55 global moored buoy sites. The overall accuracy was satisfactory, with root-mean-square difference (RMSD) of 24.05 and 10.76 Wm−2 at daily and monthly scales, respectively. However, an inconsistency issue was found in the long-term variations in the J-OFURO3 sea-surface values in approximately 2000; this inconsistency may be due to the replacement of the input dataset. To address this issue, a simple but effective inconsistency correction method was developed and conducted in this study. The analysis results demonstrated that the variations in the corrected J-OFURO3 sea-surface data were more reasonable and that its daily validation accuracy was significantly improved by decreasing the bias from 4.67 to 0.27 Wm−2 before the year 2000. Thereby, it is suggested that the inconsistency correction method should be applied before using the J-OFURO3 sea-surface data. However, the data users still should be cautious about another discontinuity issues caused by the quality of the input dataset itself.

Assessment and Intercomparison of NOAA Daily Optimum Interpolation Sea Surface Temperature (DOISST) version 2.1

NOAA Daily Optimum Interpolation Sea Surface Temperature (DOISST) has recently been updated to v2.1 (January 2016–present). Its accuracy may impact the climate assessment, monitoring and prediction, and environment-related applications. Its performance, together with those of seven other well-known sea surface temperature (SST) products, is assessed by comparison with buoy and Argo observations in the global oceans on daily 0.25°×0.25° resolution from January 2016 to June 2020. These seven SST products are NASA MUR25, GHRSST GMPE, BoM GAMSSA, UKMO OSTIA, NOAA GPB, ESA CCI, and CMC.Our assessments indicate that biases and root-mean-square-difference (RMSDs) in reference to all buoys and all Argo floats are low in DOISST. The bias in reference to the independent 10% of buoy SSTs remains low in DOISST, but the RMSD is slightly higher in DOISST than in OSTIA and CMC. The biases in reference to the independent 10% of Argo observations are low in CMC, DOISST, and GMPE; and RMSDs are low in GMPE and CMC. The biases are similar in GAMSSA, OSTIA, GPB, and CCI whether they are compared against all buoys, all Argo, or the 10% of buoy or 10% of Argo observations, while the RMSDs against Argo observations are slightly smaller than those against buoy observations. These features indicate a good performance of DOISST v2.1 among the eight products, which may benefit from ingesting the Argo observations by expanding global and regional spatial coverage of in situ observations for effective bias correction of satellite data.